205 citations · 777 across the 38 of their papers we have counts for
41 papers
Does Zero-Shot Reinforcement Learning Exist?
Ahmed Touati, Jérémy Rapin, Yann Ollivier
A zero-shot RL agent is an agent that can solve any RL task in a given environment, instantly with no additional planning or learning, after an initial reward-free learning phase.…
Agnostic Physics-Driven Deep Learning
Benjamin Scellier, Siddhartha Mishra, Yoshua Bengio +1
This work establishes that a physical system can perform statistical learning without gradient computations, via an Agnostic Equilibrium Propagation (Aeqprop) procedure that combin…
Unbiased Methods for Multi-Goal Reinforcement Learning
Léonard Blier, Yann Ollivier
In multi-goal reinforcement learning (RL) settings, the reward for each goal is sparse, and located in a small neighborhood of the goal. In large dimension, the probability of reac…
Learning One Representation to Optimize All Rewards
Ahmed Touati, Yann Ollivier
We introduce the forward-backward (FB) representation of the dynamics of a reward-free Markov decision process. It provides explicit near-optimal policies for any reward specified…
Learning Successor States and Goal-Dependent Values: A Mathematical Viewpoint
Léonard Blier, Corentin Tallec, Yann Ollivier
In reinforcement learning, temporal difference-based algorithms can be sample-inefficient: for instance, with sparse rewards, no learning occurs until a reward is observed. This ca…
Convergence of Online Adaptive and Recurrent Optimization Algorithms
Pierre-Yves Massé, Yann Ollivier
We prove local convergence of several notable gradient descent algorithms used in machine learning, for which standard stochastic gradient descent theory does not apply directly. T…